Frontiers in FinTech: Multimodal Foundation Models for Financial Reporting and Decision Science
2026-08-24 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors developed FinVision, a system that helps process many types of financial documents like PDFs, Excel files, and images using a model that understands both language and visuals. Their method checks for consistency across different document types, learns specific financial valuation methods, and includes tools for decision-making and risk monitoring through natural language. Testing showed FinVision reduced errors in company valuations and helped professionals work faster. The authors suggest their system could improve audit processes and make expert financial analysis more accessible.
Multimodal Large Language ModelFinancial ValuationDiscounted Cash Flow (DCF)Price-Earnings Ratio (P/E)Audit AutomationPortfolio TheoryCross-modal ConsistencyRisk MonitoringFinancial ReportingNatural Language Processing
Authors
Yulu Huang, Niannian Yu, Yaxin Yang, Yong Huang
Abstract
Heterogeneous financial data spanning PDF reports, Excel statements, chart images, and scanned policy documents challenge accounting information systems (AIS). This study introduces FinVision, a multimodal large language model (MLLM) system integrating vision-language models with domain-specific financial reasoning. Three innovations: (1) multimodal document intelligence with an automated cross-modal consistency validator mirroring audit evidence corroboration; (2) domain-adaptive two-stage training mastering valuation methodologies (DCF, P/E, P/B, P/S); and (3) a natural-language decision pipeline integrating modern portfolio theory, real-time risk monitoring, and multi-turn dialogue. Validation on 200 listed companies shows a 19 percent reduction in valuation error, and a user study with 48 professionals shows a 51 percent reduction in task completion time. Implications for audit automation, financial reporting quality, and democratized expert-level analysis are discussed.